We apply hindsight relabeling [1]} to the non-goal-directed demonstrations to transform them into goal-conditioned demonstrations by assuming that the final state in the trajectory was the goal the policy was actually trying to reach. Now we have \(T = ((t_1, g_1=s_{1n}), (t_2, , g_2=s_{2n}) ..., (t_N, g_N=s_{Nn}))\) . Finally, the dataset is augmented each training step by randomly sampling a sub-sequence of length \(k\) . In simulation we set \(k=96\) , and in the real world we set \(k=16\) . In both we ensure that the timestamp of the ball hit point is included in the sampled sub-sequence.
